NVIDIA launched a new visual computing appliance called the Iray VCA at the GPU Technology Conference last week. This new piece of enterprise hardware uses full GK 110 graphics cards to accelerate the company’s Iray renderer which is used to create photo realistic models in various design programs.

The Iray VCA specifically is a licensed appliance (hardware + software) that combines NVIDIA hardware and software. On the hardware side of things, the Iray VCA is powered by eight graphics cards, dual processors (unspecified but likely Intel Xeons based on usage in last year’s GRID VCA), 256GB of system RAM, and a 2TB SSD. Networking hardware includes two 10GbE NICs, two 1GbE NICs, and one Infiniband connection. In total, the Iray VCA features 20 CPU cores and 23,040 CUDA cores. The GPUs used are based on the full GK110 die and are paired with 12GB of memory each.

Even better, it is a scalable solution such that companies can add additional Iray VCAs to the network. The appliances reportedly transparently accelerate the Iray accelerated renders done on designer’s workstations. NVIDIA reports that an Iray VCA is approximately 60-times faster than a Quadro K5000-powered workstation. Further, according to NVIDIA, 19 Iray VCAs working together amounts to 1 PetaFLOP of compute performance which is enough to render photo realistic simulations using 1 billion rays with up to hundreds of thousands of bounces.

The Iray VCA enables some rather impressive real time renders of 3D models with realistic physical properties and lighting. The models are light simulations that use ray tracing, global illumination and other techniques to show photo realistic models using up to billions of rays of light. NVIDIA is positioning the Iray VCA as an alternative to physical prototyping, allowing designers to put together virtual prototypes that can be iterated and changed at significantly less cost and time.

Iray itself is NVIDIA’s GPU-accelerated photo realistic renderer. The Iray technology is used in a number of design software packages. The Iray VCA is meant to further accelerate that Iray renderer by throwing massive amounts of parallel processing hardware at the resource intensive problem over the network (the Iray VCAs can be installed at a data center or kept on site). Initially the Iray VCA will support 3ds Max, Catia, Bunkspeed, and Maya, but NVIDIA is working on supporting all Iray accelerated software with the VCA hardware.

The virtual prototypes can be sliced and examined and can even be placed in real world environments by importing HDR photos. Jen-Hsun Huang demonstrated this by placing Honda’s vehicle model on the GTC stage (virtually).

In fact, one of NVIDIA’s initial partners with the Iray VCA is Honda. Honda is currently beta testing a cluster of 25 Iray VCAs to refine styling designs for cars and their interiors based on initial artistic work. Honda Research and Development System Engineer Daisuke Ide was quoted by NVIDIA as stating that “Our TOPS tool, which uses NVIDIA Iray on our NVIDIA GPU cluster, enables us to evaluate our original design data as if it were real. This allows us to explore more designs so we can create better designs faster and more affordably.”

The Iray VCA (PDF) will be available this summer for $50,000. The sticker price includes the hardware, Iray license, and the first year of updates and maintenance. This is far from consumer technology, but it is interesting technology that may be used in the design process of your next car or other major purchase.

What do you think about the Iray VCA and NVIDIA's licensed hardware model?

NVIDIA started the Emerging Companies Summit six years ago, and since then the event has grown in size and scope to identify and support those technology companies tha leverage (or plan to leverage) GPGPU computing to deliver innovative products. The ECS continues to be a platform for new startups to showcase their work at the annual GPU Technology Conference. NVIDIA provides support in the form of legal, developmental, and co-marketing to the companies featured at ECS.

There was an interesting twist this year though in the form of the Early Start Challenge. This is a new aspect to ECS in addition to the ‘One to Watch’ award. I attended the Emerging Companies Summit again this year and managed to snag some photos and participate in the Early Start Challenge (disclosure: i voted for Audiostream TV).

The 12 Early Start Challenge contestants take the stage at once to await the vote tally.

During the challenge, 12 selected startup companies were each given eight minutes on stage to pitch their company and why their innovations were deserving of the $100,000 grand prize. The on stage time was divided into a four minute presentation and a four minute Q&A session with the panel of judges (this year the audience was not part of the Q&A session at ECS unlike last year due to time constraints).

After all 12 companies had their chance on stage, the panel of judges and the audience submitted their votes for the most innovative startup. The panel of judges included:

Map-D is a company that specializes in a scaleable in-memory GPU database that promises millisecond queries directly from GPU memory (with GPU memory bandwidth being the bottleneck) and very fast database inserts. The company is working with Facebook and PayPal to analyze data. In the case of Facebook, Map-D is being used to analyze status updates in real time to identify malicious behavior. The software can be scaled across eight NVIDIA Tesla cards to analyze a billion Twitter tweets in real time.

It is specialized software, but extremely useful within its niche. Hopefully the company puts the prize money to good use in furthering its GPGPU endeavors. Although there was only a single grand prize winner, I found all the presentations interesting and look forward to seeing where they go from here.

NVIDIA recently unified its desktop and mobile GPU lineups by moving to a Kepler-based GPU in its latest Tegra K1 mobile SoC. The move to the Kepler architecture has simplified development and enabled the CUDA programming model to run on mobile devices. One of the main points of the opening keynote earlier today was ‘CUDA everywhere,’ and NVIDIA has officially accomplished that goal by having CUDA compatible hardware from servers to desktops to tablets and embedded devices.

Speaking of embedded devices, NVIDIA showed off a new development board called the Jetson TK1. This tiny new board features a NVIDIA Tegra K1 SoC at its heart along with 2GB RAM and 16GB eMMC storage. The Jetson TK1 supports a plethora of IO options including an internal expansion port (GPIO compatible), SATA, one half-mini PCI-e slot, serial, USB 3.0, micro USB, Gigabit Ethernet, analog audio, and HDMI video outputs.

Of course the Tegra K1 part is a quad core (4+1) ARM CPU and a Kepler-based GPU with 192 CUDA cores. The SoC is rated at 326 GFLOPS which enables some interesting compute workloads including machine vision.

In fact, Audi has been utilizing the Jetson TK1 development board to power its self-driving prototype car (more on that soon). Other intended uses for the new development board include robotics, medical devices, security systems, and perhaps low power compute clusters (such as an improved Pedraforca system).It can also be used as a simple desktop platform for testing and developing mobile applications for other Tegra K1 powered devices, of course.

Beyond the hardware, the Jetson TK1 comes with the CUDA toolkit, OpenGL 4.4 driver, and NVIDIA VisionWorks SDK which includes programming libraries and sample code for getting machine vision applications running on the Tegra K1 SoC.

The Jetson TK1 is available for pre-order now at $192 and is slated to begin shipping in April. Interested developers can find more information on the NVIDIA developer website.

During the opening keynote, NVIDIA showed off several pieces of hardware that will be available soon. On the desktop and workstation side of things, researchers (and consumers chasing the ultra high end) have the new GTX Titan Z to look forward to. This new graphics card is a dual GK110 GPU monster that offers up 8 TeraFLOPS of number crunching performance for an equally impressive $2,999 price tag.

Specifically, the GTX TITAN Z is a triple slot graphics card that marries two full GK110 (big Kepler) GPUs for a total of 5,760 CUDA cores, 448 TMUs, and 96 ROPs with 12GB of GDDR5 memory on a 384-bit bus (6GB on a 384-bit bus per GPU). NVIDIA has yet to release clockspeeds, but the two GPUs will run at the same clocks with a dynamic power balancing feature. Four the truly adventurous, it appears possible to SLI two GTX Titan Z cards using the single SLI connector. Display outputs include two DVI, one HDMI, and one DisplayPort connector.

NVIDIA is cooling the card using a single fan and two vapor chambers. Air is drawn inwards and exhausted out of the front exhaust vents.

In short, the GTX Titan Z is NVIDIA's new number crunching king and should find its way into servers and workstations running big data analytics and simulations. Personally, I'm looking forward to seeing someone slap two of them into a gaming PC and watching the screen catch on fire (not really).